% This file was created with JabRef 2.9.2.
% Encoding: UTF8

@INPROCEEDINGS{vanBeers2005simulation,
  author = {van Beers, W.C.M.},
  title = {Kriging metamodeling in discrete-event simulation: an overview},
  booktitle = {Simulation Conference, 2005 Proceedings of the Winter},
  year = {Dec.},
  pages = {7 pp.-},
  abstract = {Many simulation experiments require considerable computer time, so
	interpolation is needed for sensitivity analysis and optimization.
	The interpolating functions are 'metamodels' (or 'response surfaces')
	of the underlying simulation models. For sensitivity analysis and
	optimization, simulationists use different interpolation techniques
	(e.g. low-order polynomial regression or neural nets). This paper,
	however, focuses on Kriging interpolation. In the 1950's, D.G. Krige
	developed this technique for the mining industry. Currently, Kriging
	interpolation is frequently applied in computer aided engineering.
	In discrete event simulation, however, Kriging has just started.
	This paper discusses Kriging for sensitivity analysis in simulation,
	including methods to select an experimental design for Kriging interpolation.},
  doi = {10.1109/WSC.2005.1574252},
  file = {:H\:\\Mes Documents\\articles\\selected\\2005 Kriging Metamodeling in discrete event simulation an overview.pdf:PDF},
  keywords = {discrete event simulation;interpolation;Kriging interpolation technique;Kriging
	metamodeling;discrete event simulation;interpolating function;metamodel;sensitivity
	analysis;simulation experiment model;Analytical models;Computational
	modeling;Computer simulation;Discrete event simulation;Interpolation;Metamodeling;Neural
	networks;Polynomials;Response surface methodology;Sensitivity analysis}
}

@ARTICLE{vanBeers2008Kriging,
  author = {Wim C.M. van Beers and Jack P.C. Kleijnen},
  title = {Customized sequential designs for random simulation experiments:
	Kriging metamodeling and bootstrapping},
  journal = {European Journal of Operational Research},
  year = {2008},
  volume = {186},
  pages = {1099 - 1113},
  number = {3},
  abstract = {This paper proposes a novel method to select an experimental design
	for interpolation in random simulation, especially discrete event
	simulation. (Though the paper focuses on Kriging, this design approach
	may also apply to other types of metamodels such as non-linear regression
	models and splines.) Assuming that simulation requires much computer
	time, it is important to select a design with a small number of observations
	(or simulation runs). The proposed method is therefore sequential.
	Its novelty is that it accounts for the specific input/output behavior
	(or response function) of the particular simulation at hand; i.e.,
	the method is customized or application-driven. A tool for this customization
	is bootstrapping, which enables the estimation of the variances of
	predictions for inputs not yet simulated. The method is tested through
	two classic simulation models, namely the expected steady-state waiting
	time of the M/M/1 queuing model, and the mean costs of a terminating
	(s, S) inventory simulation. For these two simulation models the
	novel design indeed gives better results than a popular alternative
	design, namely Latin Hypercube Sampling (LHS) with a prefixed sample.},
  doi = {10.1016/j.ejor.2007.02.035},
  file = {:H\:\\Mes Documents\\articles\\selected\\2004 Customized sequential designs fro random simulation experiments kriging metamodeling and bootstrapping.pdf:PDF},
  issn = {0377-2217},
  keywords = {Simulation},
  url = {http://www.sciencedirect.com/science/article/pii/S0377221707002895}
}

@ARTICLE{Benes1963,
  author = {V. E. Benes},
  title = {A Thermodynamic theory of traffic in connecting networks},
  journal = {Bell System Technical Journal},
  year = {1963},
  volume = {42},
  pages = {567-607},
  file = {:H\:\\Mes Documents\\articles\\metamodeling\\1962 A thermodynamic theory of traffic in connecting networks.pdf:PDF}
}

@INPROCEEDINGS{Bent2004,
  author = {Bent, Russell and Van Hentenryck, Pascal},
  title = {Regrets only! online stochastic optimization under time constraints},
  booktitle = {Regrets only! online stochastic optimization under time constraints},
  year = {2004},
  series = {AAAI'04},
  pages = {501--506},
  publisher = {AAAI Press},
  acmid = {1597230},
  file = {:H\:\\Mes Documents\\articles\\selected\\2004 Regrets only online stochastic optimization under time constraints.pdf:PDF},
  isbn = {0-262-51183-5},
  location = {San Jose, California},
  numpages = {6},
  url = {http://dl.acm.org/citation.cfm?id=1597148.1597230}
}

@ARTICLE{BenTel2002,
  author = {Aharon Ben-Tal and Arkadi Nemirovski},
  title = {Robust optimization - methodology and applications},
  journal = {Math. Program.},
  year = {2002},
  volume = {92},
  pages = {453-480},
  number = {3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://dx.doi.org/10.1007/s101070100286}
}

@ARTICLE{BenTal1998,
  author = {A. Ben-Tal and A. Nemirovski},
  title = {Robust convex optimization},
  journal = {Mathematics of Operations Research},
  year = {1998},
  volume = {23},
  pages = {769--805},
  file = {:H\:\\Mes Documents\\articles\\selected\\Robust convex optimization.pdf:PDF}
}

@ARTICLE{Bertsimas2010app,
  author = {Bertsimas, Dimitris and Brown, David B and Caramanis, Constantine},
  title = {Theory and Applications of Robust Optimization},
  journal = {Operations Research},
  year = {2010},
  volume = {53},
  pages = {50},
  number = {3},
  file = {:H\:\\Mes Documents\\articles\\selected\\2010 Theory and applications of robust optimization.pdf:PDF},
  publisher = {Citeseer},
  url = {http://arxiv.org/abs/1010.5445}
}

@ARTICLE{Bertsimas2010,
  author = {Bertsimas, Dimitris and Nohadani, Omid and Teo, Kwong Meng},
  title = {Nonconvex Robust Optimization for Problems with Constraints},
  journal = {INFORMS J. on Computing},
  year = {2010},
  volume = {22},
  pages = {44--58},
  number = {1},
  month = jan,
  acmid = {1735138},
  address = {Institute for Operations Research and the Management Sciences (INFORMS),
	Linthicum, Maryland, USA},
  doi = {10.1287/ijoc.1090.0319},
  file = {:H\:\\Mes Documents\\articles\\selected\\2009 Nonconvex Robust Optimization for Problems with Constraints.pdf:PDF},
  issn = {1526-5528},
  issue_date = {Winter 2010},
  keywords = {constraints, nonconvex optimization, optimization, robust optimization},
  numpages = {15},
  publisher = {INFORMS},
  url = {http://dx.doi.org/10.1287/ijoc.1090.0319}
}

@ARTICLE{Beyer2007,
  author = {Hans-Georg Beyer and Bernhard Sendhoff},
  title = {Robust optimization A comprehensive survey},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  year = {2007},
  volume = {196},
  pages = {3190-3218},
  number = {33-34},
  abstract = {This paper reviews the state-of-the-art in robust design optimization
	the search for designs and solutions which are immune with respect
	to production tolerances, parameter drifts during operation time,
	model sensitivities and others. Starting with a short glimps of Taguchi's
	robust design methodology, a detailed survey of approaches to robust
	optimization is presented. This includes a detailed discussion on
	how to account for design uncertainties and how to measure robustness
	(i.e., how to evaluate robustness). The main focus will be on the
	different approaches to perform robust optimization in practice including
	the methods of mathematical programming, deterministic nonlinear
	optimization, and direct search methods such as stochastic approximation
	and evolutionary computation. It discusses the strengths and weaknesses
	of the different methods, thus, providing a basis for guiding the
	engineer to the most appropriate techniques. It also addresses performance
	aspects and test scenarios for direct robust optimization techniques.},
  doi = {10.1016/j.cma.2007.03.003},
  file = {:H\:\\Mes Documents\\articles\\optimization\\2007 Robust optimization  A comprehensive survey.pdf:PDF},
  issn = {0045-7825},
  keywords = {Direct search methods, robust measure, optimization},
  url = {http://www.sciencedirect.com/science/article/pii/S0045782507001259}
}

@INPROCEEDINGS{Bosman2007,
  author = {Bosman, Peter A. N. and La Poutr{\'e}, Han},
  title = {Learning and anticipation in online dynamic optimization with evolutionary
	algorithms: the stochastic case},
  booktitle = {Proceedings of the 9th annual conference on Genetic and evolutionary
	computation},
  year = {2007},
  series = {GECCO '07},
  pages = {1165--1172},
  address = {New York, NY, USA},
  publisher = {ACM},
  acmid = {1277187},
  doi = {10.1145/1276958.1277187},
  file = {:H\:\\Mes Documents\\articles\\selected\\2007 Learning and Anticipation in Online Dynamic Optimization with evolutionary algorithms the stochastic case.pdf:PDF},
  isbn = {978-1-59593-697-4},
  keywords = {anticipation, dynamic optimization, evolutionary algorithms, online
	optimization, stochastic optimization},
  location = {London, England},
  numpages = {8},
  url = {http://doi.acm.org/10.1145/1276958.1277187}
}

@INPROCEEDINGS{Dooms2008,
  author = {Dooms, Gr{\'e}goire and Van Hentenryck, Pascal},
  title = {Gap reduction techniques for online stochastic project scheduling},
  booktitle = {Proceedings of the 5th international conference on Integration of
	AI and OR techniques in constraint programming for combinatorial
	optimization problems},
  year = {2008},
  series = {CPAIOR'08},
  pages = {66--81},
  address = {Berlin, Heidelberg},
  publisher = {Springer-Verlag},
  acmid = {1786725},
  file = {:H\:\\Mes Documents\\articles\\selected\\2008 Gap Reduction Techniques for Online Stochastic Project Scheduling.pdf:PDF},
  isbn = {3-540-68154-X, 978-3-540-68154-0},
  location = {Paris, France},
  numpages = {16},
  url = {http://dl.acm.org/citation.cfm?id=1786715.1786725}
}

@INCOLLECTION{Ebenbauer2009dissipative,
  author = {C. Ebenbauer and T. Raff, and F. Allg{\"o}wer},
  title = {Dissipation inequalities in systems theory: An introduction and recent
	results},
  booktitle = {Invited Lectures of the International Congress on Industrial and
	Applied Mathematics 2007},
  publisher = {European Mathematical Society Publishing House},
  year = {2009},
  editor = {R. Jeltsch and G. Wanner},
  pages = {23--42},
  file = {:H\:\\Mes Documents\\articles\\control\\2009 Dissipation inequalities in systems theory An introduction and recent results.pdf:PDF}
}

@ARTICLE{GrosmanAutomatedMPC2002,
  author = {Benyamin Grosman and Daniel R Lewin},
  title = {Automated nonlinear model predictive control using genetic programming
	},
  journal = {Computers \& Chemical Engineering },
  year = {2002},
  volume = {26},
  pages = {631-640},
  number = {4-5},
  doi = {10.1016/S0098-1354(01)00780-3},
  file = {:H\:\\Mes Documents\\articles\\gp\\2002 Automated Nonlinear Model Predictive Control  using genetic programming.pdf:PDF},
  issn = {0098-1354},
  keywords = {Empirical process modeling},
  url = {http://www.sciencedirect.com/science/article/pii/S0098135401007803}
}

@ARTICLE{Haddad2005thermodynamic,
  author = {Haddad, M.M. and Hui, Qing and Nersesov, S.G. and Chellaboina, V.},
  title = {Thermodynamic modeling, energy equipartition, and nonconservation
	of entropy for discrete-time dynamical systems},
  journal = {Advances in Differential Equations},
  year = {2005},
  volume = {2005},
  pages = {275-318},
  number = {3},
  doi = {10.1109/ACC.2005.1470760},
  file = {:H\:\\Mes Documents\\articles\\metamodeling\\2005 Thermodynamic modeling energy equipartition and nonconservation of entropy for discrete time dynamical system.pdf:PDF},
  issn = {0743-1619},
  keywords = {Lyapunov methods;discrete time systems;entropy;large-scale systems;nonlinear
	dynamical systems;thermodynamics;Lyapunov stability;compartmental
	dynamical system theory;discrete-time dynamical systems;ectropy;energy
	equipartition;energy flow models;entropy nonconservation;large-scale
	dynamical systems;temperature equipartition;thermodynamic modeling;Aerospace
	engineering;Biological materials;Biological system modeling;Energy
	capture;Energy conservation;Energy measurement;Entropy;Large-scale
	systems;Lyapunov method;Thermodynamics}
}

@INPROCEEDINGS{Hatzakis2006eampc,
  author = {Hatzakis, Iason and Wallace, David},
  title = {Dynamic multi-objective optimization with evolutionary algorithms:
	a forward-looking approach},
  booktitle = {Proceedings of the 8th annual conference on Genetic and evolutionary
	computation},
  year = {2006},
  series = {GECCO '06},
  pages = {1201--1208},
  address = {New York, NY, USA},
  publisher = {ACM},
  acmid = {1144187},
  doi = {10.1145/1143997.1144187},
  file = {:H\:\\Mes Documents\\articles\\gp\\2006 dynamic_multi_objective_optimization_with_evolutionary_algorithms_a_forward_looking.pdf:PDF},
  isbn = {1-59593-186-4},
  keywords = {dynamic problem, evolutionary algorithm, forecast, time series analysis,
	time-changing environment},
  location = {Seattle, Washington, USA},
  numpages = {8},
  url = {http://doi.acm.org/10.1145/1143997.1144187}
}

@INCOLLECTION{Heitsch2011scen,
  author = {Heitsch, Holger and R\"{o}misch, Werner},
  title = {Scenario Tree Generation for Multi-stage Stochastic Programs},
  booktitle = {Stochastic Optimization Methods in Finance and Energy},
  publisher = {Springer New York},
  year = {2011},
  editor = {Bertocchi, Marida and Consigli, Giorgio and Dempster, Michael A.
	H.},
  volume = {163},
  series = {International Series in Operations Research and Management Science},
  pages = {313-341}
}

@ARTICLE{Iyer2001,
  author = {Iyer, Sitaram and Druschel, Peter},
  title = {Anticipatory scheduling: a disk scheduling framework to overcome
	deceptive idleness in synchronous I/O},
  journal = {SIGOPS Oper. Syst. Rev.},
  year = {2001},
  volume = {35},
  pages = {117--130},
  number = {5},
  month = oct,
  acmid = {502046},
  address = {New York, NY, USA},
  doi = {10.1145/502059.502046},
  file = {:H\:\\Mes Documents\\articles\\robust\\2001 Anticipatory scheduling A disk scheduling framework to overcome deceptive idleness in synchronous IO.pdf:PDF},
  issn = {0163-5980},
  issue_date = {Dec. 2001},
  numpages = {14},
  publisher = {ACM},
  url = {http://doi.acm.org/10.1145/502059.502046}
}

@ARTICLE{Jin2003,
  author = {Jin, R and Du, X and Chen, W},
  title = {The use of metamodeling techniques for optimization under uncertainty},
  journal = {Structural and Multidisciplinary Optimization},
  year = {2003},
  volume = {25},
  pages = {99--116},
  number = {2},
  file = {:H\:\\Mes Documents\\articles\\selected\\2003 the use of metamodeling techniques for optimization under uncertainty.pdf:PDF},
  publisher = {Springer Berlin / Heidelberg},
  url = {http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s00158-002-0277-0}
}

@INPROCEEDINGS{Julier97ukf,
  author = {Simon J. Julier and Jeffrey K. Uhlmann},
  title = {A New Extension of the Kalman Filter to Nonlinear Systems},
  booktitle = {AeroSense: The 11th Int. Symp. on Aerospace Defence Sensing, Simulation
	and Controls},
  year = {1997},
  pages = {182--193}
}

@ARTICLE{Kleijnen2009,
  author = {Jack P. C. Kleijnen},
  title = {Kriging metamodeling in simulation: A review},
  journal = {European Journal of Operational Research},
  year = {2009},
  volume = {192},
  pages = {707-716},
  number = {3},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://dx.doi.org/10.1016/j.ejor.2007.10.013},
  file = {:H\:\\Mes Documents\\articles\\optimization\\2012 Simulation-optimization via Kriging and bootstrapping - a survey.pdf:PDF},
  keywords = {simulation, optimization, gaussian process, sensitivity analysis,
	robustness, resampling}
}

@ARTICLE{Li2007,
  author = {Li, G. and Li, M. and Azarm, S. and Rambo, J. and Joshi, Y.},
  title = {Optimizing thermal design of data center cabinets with a new multi-objective
	genetic algorithm},
  journal = {Distributed and Parallel Databases},
  year = {2007},
  volume = {21},
  pages = {167-192},
  note = {10.1007/s10619-007-7009-9},
  abstract = {There is an ever increasing need to use optimization methods for thermal
	design of data centers and the hardware populating them. Airflow
	simulations of cabinets and data centers are computationally intensive
	and this problem is exacerbated when the simulation model is integrated
	with a design optimization method. Generally speaking, thermal design
	of data center hardware can be posed as a constrained multi-objective
	optimization problem. A popular approach for solving this kind of
	problem is to use Multi-Objective Genetic Algorithms (MOGAs). However,
	the large number of simulation evaluations needed for MOGAs has been
	preventing their applications to realistic engineering design problems.
	In this paper, details of a substantially more efficient MOGA are
	formulated and demonstrated through a thermal analysis simulation
	model of a data center cabinet. First, a reduced-order model of the
	cabinet problem is constructed using the Proper Orthogonal Decomposition
	(POD). The POD model is then used to form the objective and constraint
	functions of an optimization model. Next, this optimization model
	is integrated with the new MOGA. The new MOGA uses a guided operation
	in addition to conventional genetic algorithm operations to search
	the design space for global optimal design solutions. This approach
	for optimal design is essential to handle complex multi-objective
	situations, where the optimal solutions may be non-obvious from simple
	analyses or intuition. It is shown that in optimizing the data center
	cabinet problem, the new MOGA outperforms a conventional MOGA by
	estimating the Pareto front using 50% fewer simulation calls, which
	makes its use very promising for complex thermal design problems.},
  affiliation = {University of Maryland Department of Mechanical Engineering College
	Park MD 20742 USA},
  file = {:H\:\\Mes Documents\\articles\\optimization\\2007 Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm.pdf:PDF},
  issn = {0926-8782},
  issue = {2},
  keyword = {Computer Science},
  keywords = {multi-objective, genetic algorithm, design, simulation},
  publisher = {Springer Netherlands},
  url = {http://dx.doi.org/10.1007/s10619-007-7009-9}
}

@INPROCEEDINGS{Li2005,
  author = {Li, Mian and Azarm, Shapour and Aute, Vikrant},
  title = {A multi-objective genetic algorithm for robust design optimization},
  booktitle = {Proceedings of the 2005 conference on Genetic and evolutionary computation},
  year = {2005},
  series = {GECCO '05},
  pages = {771--778},
  address = {New York, NY, USA},
  publisher = {ACM},
  acmid = {1068140},
  doi = {10.1145/1068009.1068140},
  file = {:H\:\\Mes Documents\\articles\\robust\\2005 A Multi Objective Genetic Algorithm for robust design optimization.pdf:PDF},
  isbn = {1-59593-010-8},
  keywords = {multi-objective genetic algorithms, performance and robustness trade-off,
	robust design optimization,multi-objective, evolutionary algorithm,
	genetic algorithm, robust,},
  location = {Washington DC, USA},
  numpages = {8},
  url = {http://doi.acm.org/10.1145/1068009.1068140}
}

@TECHREPORT{Liebchen2007,
  author = {Christian Liebchen and Marco Lübbecke and Rolf H. Möhring and Sebastian
	Stiller},
  title = {Recoverable {R}obustness},
  year = {2007},
  file = {:H\:\\Mes Documents\\articles\\robust\\2007 Recoverable robustness.pdf:PDF}
}

@ARTICLE{Stinstra2008experiments,
  author = {Erwin Stinstra and Dick den Hertog},
  title = {Robust optimization using computer experiments},
  journal = {European Journal of Operational Research},
  year = {2008},
  volume = {191},
  pages = {816 - 837},
  number = {3},
  doi = {10.1016/j.ejor.2007.03.048},
  file = {:H\:\\Mes Documents\\articles\\selected\\2005 Robust optimization using computer experiments.pdf:PDF},
  issn = {0377-2217},
  keywords = {Computer simulation},
  url = {http://www.sciencedirect.com/science/article/pii/S0377221707004316}
}

@PHDTHESIS{Stinstra2006thesis,
  author = {Erwin Diederik Stinstra},
  title = {The metamodel approach for simulation based design optimization},
  school = {Tilburg university},
  year = {2006},
  address = {the Netherlands}
}

@INPROCEEDINGS{Tan2008,
  author = {Tan, Kay Chen and Goh, Chi Keong},
  title = {Handling uncertainties in evolutionary multi-objective optimization},
  booktitle = {Proceedings of the 2008 IEEE world conference on Computational intelligence:
	research frontiers},
  year = {2008},
  series = {WCCI'08},
  pages = {262--292},
  address = {Berlin, Heidelberg},
  publisher = {Springer-Verlag},
  acmid = {1788931},
  file = {:H\:\\Mes Documents\\articles\\robust\\2008 Handling Uncertainties in Evolutionary multi objective optimization.pdf:PDF},
  isbn = {3-540-68858-7, 978-3-540-68858-7},
  keywords = {multi-objective, evolutionary algorithm, genetic algorithm, robust,},
  location = {Hong Kong, China},
  numpages = {31},
  url = {http://dl.acm.org/citation.cfm?id=1788915.1788931}
}

@INPROCEEDINGS{Simpson1997,
  author = {Simpson Timothy and Peplinski Jesse and Koch Patrick and Allen Janet},
  title = {On the use of statistics in design and the implications for deterministic
	computer experiments},
  year = {1997},
  pages = {1--14},
  file = {:H\:\\Mes Documents\\articles\\metamodeling\\1997 On the use of statistics in design and the implications for deterministic computer experiments.pdf:PDF},
  keywords = {d optimal, design, experiments, induction, kriging, metamodeling,
	methodology, net, neural, of, response, robust, rule, surface, taguchi,
	works}
}

@book{isaaks1989applied,
  title={An Introduction to Applied Geostatistics},
  author={Isaaks, E.H. and Srivastava, R.M.},
  isbn={9780195050134},
  lccn={lc89034891},
  url={http://books.google.fr/books?id=vC2dcXFLI3YC},
  year={1989},
  publisher={Oxford University Press}
}

@PHDTHESIS{Dellino2009thesis,
  author = {Gabriella Dellino},
  title = {Robust Simulation-Optimization Methods
using Kriging Metamodels},
  school = {Universita Degli Studi Di Bari},
  year = {2009}
}

@book{Chandrasekharan1996robust,
 author = {Chandrasekharan, P.C.}, 
 title = {Robust Control of Linear Dynamical Systems}, 
 publisher = {Academic Press}, 
 year = {1996} 
}


@TechReport{KleijnenSurvey2013,
  author={Kleijnen, Jack P.C.},
  title={Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064)},
  year={2013},
  institution={Tilburg University, Center for Economic Research},
  type={Discussion Paper},
  url={http://ideas.repec.org/p/dgr/kubcen/2013064.html},
  number={2013-064},
  abstract={Abstract: This article surveys optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the authors extensive experience with simulation-optimization through Kriging (or Gaussian process) metamodels. The analysis of these metamodels may use parametric bootstrapping for deterministic simulation or distribution-free bootstrapping (or resampling) for random simulation. The survey covers: (1) Simulation-optimization through \&quot;efficient global optimization\&quot; (EGO) using \&quot;expected improvement\&quot; (EI); this EI uses the Kriging predictor variance, which can be estimated through parametric bootstrapping accounting for estimation of the Kriging parameters. (2) Optimization with constraints for multiple random simulation outputs and deterministic inputs through mathematical programming applied to Kriging metamodels validated through distribution-free bootstrapping. (3) Taguchian robust optimization for uncertain environments, using mathematical programming applied to Kriging metamodels and distribution- free bootstrapping to estimate the variability of the Kriging metamodels and the resulting robust solution. (4) Bootstrapping for improving convexity or preserving monotonicity of the Kriging metamodel.},
  keywords={simulation; optimization; stochastic process; non-linear programming; risk}
}

@INPROCEEDINGS{Yu2010ROOT,
  author={Xin Yu and Yaochu Jin and Ke Tang and Xin Yao},
  booktitle={Evolutionary Computation (CEC), 2010 IEEE Congress on},
  title={Robust optimization over time - A new perspective on dynamic optimization problems},
  year={2010},

  pages={1-6},

  keywords={optimisation;dynamic optimization problem;moving optima;robust optimization over time problem;Aerodynamics;Benchmark testing;Optimization;Robustness;Silicon;Time measurement;Uncertainty},
  doi={10.1109/CEC.2010.5586024}
}